Faculty Bibliography

Introduction: Cytotoxic drugs are hampered by limited efficacy. Hence, a personalized treatment approach matching chemotherapy with appropriate patients remains an unmet need. Genomic heterogeneity creates an opportunity to discern key genomic aberrations and pathways that confer resistance and response to standard treatment options. We conducted a study using the Cellworks Computational Omics Biology Model (CBM) to identify novel genomic biomarkers associated with response among Non-Small Cell Lung Cancer (NSCLC) patients receiving platinum-based treatments.
Method(s): 104 NSCLC patients who received platinum-based chemotherapy were selected from TCGA: platinum-etoposide (N=18), platinum-gemcitabine (N=20), platinum-vinorelbine (N=31), platinum-paclitaxel (N=21), and platinum-docetaxel (N=14). Mutation and CNV from each case served as input for the CBM to generate a patient-specific protein network-map based on PubMed and other resources. Biomarkers unique to each patient were identified within protein network-maps. Drug impact on the disease network was biosimulated to determine efficacy score by measuring the effect of chemotherapy on the cell growth score, a composite of cell proliferation, viability, apoptosis, metastasis, DNA damage and other cancer hallmarks. Effectively, the mechanism of action of each drug was mapped to each patient's genome and biological consequences determined response.
Result(s): Among the 104 patients, 74 were responders (R) and 30 non-responders (NR), determined using compete and partial response based on RECIST criteria (Figure 1). The CBM predicted clinical response with 73% sensitivity and 77% specificity. Cellworks CBM identified novel biomarkers responsible for platinum-based combination therapy response as mentioned below. +Etoposide: 13q-del, RB1-del, MBD1-del, LIG4-del, ERCC5-del, ATP7B-del +Gemcitabine: AKT3-amp, MAPKAP2-amp, TAP1-del +Vinorelbine: TET2-del/LOF, TRIB3-amp, SLX4-del +Paclitaxel: KLF4-del, SNCG-del, RAC1-amp/GOF +Docetaxel: BCL2L1-amp, HMGA1-amp, NSD1-del, SLC22A7-amp, FSIP1-del These genes contributed to drug efficacy by impacting various pathways, including DNA repair, oxidative-stress, methylation machinery, spindle formation, and mitotic-catastrophe. The aberration frequency of these genes was high among the responders within each subgroup and was very low in non-responders. Additionally, a model of clinical outcome versus the linear and quadratic function of efficacy score, drug combination and the interaction of both showed that efficacy score provides predictive information above and beyond the choice of drug combination alone (likelihood ratio chi-sq = 35.56, df=13, p-value = 0.0007). [Formula presented]
Conclusion(s): This pilot study highlights how the Cellworks CBM biosimulation platform can help identify patients for therapy response prediction. By using novel biomarkers, a CBM-informed decision tree can be employed to identify the optimal drug combination for platinum-based therapy. We suggest that this approach be validated prospectively in a larger patient cohort. Keywords: Cancer Therapy Biosimulation, Multi-omics Therapy Biosimulation, Personalized Cancer Therapy
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Introduction: KRAS is a frequent oncogenic driver in solid tumors, including Non-Small Cell Lung Cancer (NSCLC). KRAS is involved in various signaling pathways that could allow for targeting of KRAS by targeting downstream key transcription factors that mediate oncogene signaling. At the same time, co-occurrence of other mutations alters the signaling pathways and the key transcription factors involved in the disease network. The convergence of these dysregulated pathways to activate key kinases and transcription factors defines master regulators, forming the regulatory logic (i.e. oncotecture) of the tumor cell that maintains the malignant phenotype, its hallmark behaviors, and homeostasis in the face of treatment, while also providing a new set of potential therapeutic targets. In this study, we describe the impact of KRAS and a variety of co-mutations on the tumor's oncotecture.
Method(s): 248 NSCLC patients with KRAS mutations were selected from TCGA: 110 had only KRAS mutations, 138 had co-occurrence of other mutations: EGFR 55, MET 44, and PIK3CA 39. Mutation and CNV from each case served as input for the Cellworks Computational Omics Biology Model (CBM) to generate a patient-specific protein network map from PubMed and other resources. Disease-biomarkers unique to each patient were identified within protein network maps. The CBM identified the top 25 master regulators for each patient. We calculated the frequency of occurrence of each of the selected master regulators for KRAS alone and KRAS with other mutation co-occurrences.
Result(s): Comparative networks analyses of KRAS alone and KRAS with another mutation were performed. We identified that FOXM1, IKBKB, PLK1, PAK1, MTOR, AURKA, PIK3CA, CSNK2A1 function as master regulators in more than 70% of KRAS-only mutated cancers (Table 1). When co-mutations were present, the master regulator network was upregulated, e.g.: (1) MET: RPS6KA3, STAT3, NEK2 (2) EGFR: NFKB1, CEBPA, NEK2 (3) PIK3CA: SRPK1, GLI2, AKT [Formula presented]
Conclusion(s): The Cellworks CBM can reveal transcription factor addiction by identifying the convergence points of numerous upstream dysregulated pathways. These master regulators reveal another set of potential treatment vulnerabilities or Achilles heels in the network that can inform specific treatment options. This study identifies the key transcriptional mediators of KRAS mutations and how they are shuffled by the presence of co-mutations in other common oncogenes. The CBM biosimulation platform identifies the regulatory network in the cancer laying the foundation for new therapeutic strategies targeting key master regulators. Keywords: Personalized Cancer Therapy, Cancer Therapy Biosimulation, Multi-omics Therapy Biosimulation
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Introduction: Sotorasib is a first-in-class small molecule that specifically and irreversibly inhibits KRAS^G12C. The phase 1/2 CodeBreaK 100 trial evaluated sotorasib in patients with pretreated advanced non-small cell lung cancer (NSCLC) harboring KRAS p.G12C. In the registrational phase 2 part, sotorasib showed an objective response rate (ORR) of 37.1% and a median progression-free survival (PFS) of 6.8 months. Here, we report on the activity of sotorasib in patients with treated brain metastases (BM).
Method(s): Patients from the phase 1/2 CodeBreaK 100 trial receiving 960mg dose were included. Patients with active untreated BM were excluded. Patients who had BM resected or had received radiation therapy ending >=4 weeks prior to the trial were eligible. Systemic response was assessed by independent central review per RECIST 1.1. The presence of neurologically stable/asymptomatic BM at baseline was determined by investigators. CNS response was retrospectively evaluated by central neuroradiologic review, using the response assessment in neuro-oncology BM (RANO-BM) criteria, in patients with >=1 target CNS lesions (>= 10mm) and/or non-target CNS lesions. For non-target lesions, stable disease (SD) refers to response that is neither complete response (CR) nor progressive disease (PD).
Result(s): 174 patients were included: 40 had stable BM (23.0%) while 134 (77.0%) had no BM at baseline. In the BM group, 65% had received prior radiotherapy, and 20% had received prior brain surgery. Systemic efficacy of sotorasib per RECIST 1.1 is shown in the Table. Per central RANO-BM review, 16 patients had baseline and >=1 on-treatment evaluable scans: 3 had target and 13 had non-target CNS lesions. 9 patients had 1 lesion, 2 had 4 lesions, and 5 had >=5 lesions. Of 13 patients with non-target CNS lesions, 2 had CR, 11 had SD. Of 3 patients with target lesions, 1 had SD, and 2 had PD. Overall, intracranial disease control was achieved in 14 of 16 patients (87.5%) with evaluable BM. Safety in the BM group was consistent with previous reports. [Formula presented]
Conclusion(s): Sotorasib demonstrated systemic durable anticancer activity, with a median PFS and OS of 5.3 and 8.3 months in NSCLC patients with stable BM previously treated with radiation or surgery. Intracranial complete responses were observed, with continued intracranial stabilization observed in the majority of patients with evaluable BM. Additional studies are ongoing to evaluate sotorasib in patients with active untreated BM (NCT04185883). Keywords: brain metastases, KRAS p.G12C, sotorasib (AMG 510)
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Introduction: Gemcitabine and carboplatin/cisplatin ("platinum")-based combinations are used to treat a wide variety of malignancies including gynecologic, breast, lung, and occult primary cancers. In Non-Small Cell Lung Cancer (NSCLC), these combinations led to a substantial improvement in overall survival. Nevertheless, a large proportion of patients do not respond. An optimal cytotoxic strategy for managing NSCLC and the discovery of predictive biomarkers for cytotoxic chemotherapy to guide treatment selection remain unmet needs in the clinic. The Cellworks Computational Omics Biology Model (CBM) platform identified a unique chromosomal signature which permits a stratification of patients that are most likely to respond to gemcitabine and platinum treatments.
Method(s): Twenty patients treated with gemcitabine and platinum were identified from a TCGA dataset and analyzed. The mutation and copy number aberrations from individual cases served as input into the CBM to generate a patient-specific protein network map from PubMed and other online resources. Disease-biomarkers unique to each patient were identified within patient-specific protein network maps. Digital drug biosimulations were conducted by measuring the effect of gemcitabine and platinum on a cell growth score comprised of a composite of cell proliferation, viability, apoptosis, metastasis, and other cancer hallmarks. Drug biosimulations were conducted by mapping the drug combination to the patient genome along with a rational mechanism of action and validated based on the patient's genomic profile and biological consequences.
Result(s): Of the 20 patients treated with gemcitabine and platinum, 12 had clinical responses while 8 were non-responders. The CBM correctly predicted response in 17/20 patients with 85% accuracy, 63% specificity and 100% sensitivity. The CBM identified that novel amplified segments of Chromosome 6p were associated with non-responsiveness to gemcitabine and platinum therapy. Key genes on these segments include E2F3, MDC1, TAP1 and TNF. Amplification of E2F3 leads to activation of MSH2/6, which enhances mismatch repair thereby causing resistance. Amplification of MDC1 leads to activation of CHECK2, BRCA1, ATM, and NBN_RAD51_MRE1 Complex which stimulates homologous recombination repair. Amplification of TAP1 reduces gemcitabine transport. Besides 6p amplification, PRMT7 deletion was also associated with gemcitabine resistance. Notably, BRCA2-del, RB1-del, NPM1-del, LIG4-del, XRCC4-del, RAD50-del, ATRX-Del, RBBP8-del, XRCC6-del, and FBXW7-del were also prevalent among gemcitabine non-responders. Interestingly, these aberrations also happen to be key criteria for predicting response to etoposide. Therefore, etoposide and platinum combinations might have provided better disease control for these patients.
Conclusion(s): Amplification of chromosome 6p appears to be an important cause of treatment failure for patients receiving gemcitabine-platinum combinations. In this small patient group, the Cellworks CBM was especially useful for identifying non-responders. Biosimulation can identify novel patient subgroups for therapy response prediction and has promise to help select more effective therapies. Keywords: Multi-omics Therapy Biosimulation, Personalized Cancer Therapy, Cancer Therapy Biosimulation
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Introduction: PD-L1 is an immune checkpoint protein that mediates immune evasion. In Non-Small Cell Lung Cancer (NSCLC), its expression is used to predict the outcome of treatment targeting PD-1/L1. However, clinical benefits do not occur uniformly, and new approaches are needed to assist in selecting patients for immunotherapy.
Method(s): 26 patients with known clinical response to pembrolizumab were selected from publicly available data (PMID:25765070) (Table 1). Mutation and copy number aberrations from individual cases served as input into the Cellworks Omics Biology Model (CBM) to generate a patient-specific protein network map from PubMed and other online resources. Disease-biomarkers unique to each patient were identified within protein network maps. Digital drug biosimulations were conducted by measuring the effect of pembrolizumab on a cell growth score comprised of a composite of cell proliferation, viability, apoptosis, metastasis, and other cancer hallmarks. Drug biosimulations were conducted to identify and evaluate therapeutic efficacy.
Result(s): Among 26 patients treated with pembrolizumab, 14 were clinical responders, defined as stable disease or partial response lasting longer than 6 months, and 12 non-responders. Notably, 9/12 non-responders were PD-L1 positive (Table 1). Cellworks biosimulation predicted response with 84.6% accuracy, 75% specificity, and 92.86% sensitivity. Positive predictive value was 81.25% and negative predictive value was 90%. CBM identified that response was influenced by pathways that impacted the tumor microenvironment (TME). Deletions of adenosine pathway genes were observed in responders, whereas CNVs for these genes were enriched in non-responders (Table 1). Loss of ENPP1, and INSIG1 and SENP2 CNV aberrations, all of which regulate the STING pathway, could play a significant role in governing immune checkpoint blockade (ICB) response. Although STK11 loss appears to be a biomarker for poor ICB response, it was equally enriched in responders (n=7) and non-responders (n=7) in this dataset. Notably, responders had STK11 mutations and chromosome 6 loss, whereas non-responders had STK11 loss with chromosome 6 wild type or gain. Finally, frameshift mutations (FSM) enhance neoepitope formation and were higher in responders (average FSM = 48) vs non-responders (average FSM = 19). [Formula presented]
Conclusion(s): Alterations of the adenosine and STING pathways play key roles in determining benefit from PD-1/L1 targeting and highlight therapeutic possibilities for improving outcome in specific patient subgroups based on PD-L1 expression. The Cellworks CBM captures a holistic picture of the TME using tumor omics and improves response prediction beyond PD-L1 testing. Keywords: Multi-omics Therapy Biosimulation, Personalized Cancer Therapy, Immunotherapy Biosimulation
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Introduction: Sotorasib is a first-in-class small molecule that specifically and irreversibly inhibits KRAS^G12C. In the registrational phase 2 CodeBreaK 100 trial, sotorasib showed an objective response rate (ORR) of 37.1% and a median progression-free survival (PFS) of 6.8 months in patients with KRAS p.G12C-mutated non-small cell lung cancer (NSCLC) previously treated with platinum-based chemotherapy and/or immunotherapy. Response to sotorasib has been observed across co-occurring mutational profiles. Here, we report preliminary data on the genomic profiles and potential determinants of response to sotorasib from an exploratory analysis of this trial.
Method(s): Baseline tissue samples were collected and analyzed for genomic alterations in KEAP1, the upstream RTK pathway, and the downstream PI3K/AKT/mTOR and MAPK pathways. Patients were categorized into the following 3 groups: early progressors (patients with an event of progressive disease and PFS of <3 months), late progressors (patients with an event of progressive disease and PFS of >= 3 months), non-progressors (patients with no event of progressive disease and PFS of >= 3 months).
Result(s): A total of 126 patients were enrolled into the phase 2 trial. 65 patients with available data from baseline tissue samples were categorized per methods: 22 early progressors, 23 late progressors, and 20 non-progressors. 11 of the 65 patients had KEAP1 mutations (7 in the early progressor group, 2 in the late progressor group, and 2 in the non-progressor group). In the early progressor group, we observed mutations in EGFR, FGFR, PDGFR, RET, and MET, which were also identified in other groups. Among the patients with mutations in the MAPK pathway genes, the late progressor group was the most prevalent (43%, n=6), followed by early progressor (29%, n=4) and non-progressor (29%, n=4). Among patients with mutations in genes of the PI3K/AKT/mTOR pathway, late progressor and early progressor groups were the most prevalent (36%, n=9, each), followed by non-progressor group (28%, n=7) (summary Table below). [Formula presented]
Conclusion(s): In this descriptive biomarker analysis of baseline tissue specimens from the phase 2 CodeBreaK 100 trial of sotorasib in KRAS p.G12C-mutated NSCLC, diverse mutation patterns were observed. No unique genomic profiles were identified in patient groups. The presence of KEAP1 mutation was observed across all groups and was more prevalent in early progressors. These findings warrant further investigation of the longitudinal cfDNA dynamics in patients receiving sotorasib. Keywords: biomarkers, sotorasib (AMG 510), KRAS p.G12C NSCLC
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BACKGROUND:Immune checkpoint inhibitors (ICIs) can cause life-threatening cardiovascular adverse events (CVAEs) that may not be attributed to therapy. The outcomes of clinical trials may underestimate treatment-related adverse events due to restrictive eligibility, limited sample size, and failure to anticipate selected toxicities. We evaluated the incidence and clinical determinants of CVAEs in real-world population on ICI therapy. PATIENTS AND METHODS:Among 2 687 301 patients diagnosed with cancer from 2011 to 2018, 16 574 received ICIs for any cancer. Patients in the ICI and non-ICI cohorts were matched in a 1 : 1 ratio according to age, sex, National Cancer Institute comorbidity score, and primary cancer. The non-ICI cohort was stratified into patients who received chemotherapy (N = 2875) or targeted agents (N = 4611). All CVAEs, non-cardiac immune-related adverse events occurring after treatment initiation, baseline comorbidities, and treatment details were identified and analyzed using diagnosis and billing codes. RESULTS:Median age was 61 and 65 years in the ICI and non-ICI cohorts, respectively (P < 0.001). ICI patients were predominantly male (P < 0.001). Lung cancer (43.1%), melanoma (30.4%), and renal cell carcinoma (9.9%) were the most common cancer types. CVAE diagnoses in our dataset by incidence proportion (ICI cohort) were stroke (4.6%), heart failure (3.5%), atrial fibrillation (2.1%), conduction disorders (1.5%), myocardial infarction (0.9%), myocarditis (0.05%), vasculitis (0.05%), and pericarditis (0.2%). Anti-cytotoxic T-lymphocyte-associated protein 4 increased the risk of heart failure [versus anti-programmed cell death protein 1; hazard ratio (HR), 1.9; 95% confidence interval (CI) 1.27-2.84] and stroke (HR, 1.7; 95% CI 1.3-2.22). Pneumonitis was associated with heart failure (HR, 2.61; 95% CI 1.23-5.52) and encephalitis with conduction disorders (HR, 4.35; 95% CI 1.6-11.87) in patients on ICIs. Advanced age, primary cancer, nephritis, and anti-cytotoxic T-lymphocyte-associated protein 4 therapy were commonly associated with CVAEs in the adjusted Cox proportional hazards model. CONCLUSIONS:Our findings underscore the importance of risk stratification and cardiovascular monitoring for patients on ICI therapy.

BACKGROUND:We developed and validated a prognostic and predictive computational pathology risk score (CoRiS) using H&E stained tissue images from patients with early-stage non-small cell lung cancer (ES-NSCLC). METHODS:which comprised surgery + chemotherapy were used to validate CoRiS as predictive of added benefit to adjuvant chemotherapy (ACT) by comparing survival between different CoRiS defined risk groups. FINDINGS/RESULTS:(HR = 0.46, adj. P = .08). INTERPRETATION/CONCLUSIONS:CoRiS is a tissue non-destructive, quantitative and low-cost tool that could potentially help guide management of ES-NSCLC patients.

BACKGROUND:p.G12C-mutated advanced solid tumors in a phase 1 study, and particularly promising anticancer activity was observed in a subgroup of patients with non-small-cell lung cancer (NSCLC). METHODS:p.G12C-mutated advanced NSCLC previously treated with standard therapies. The primary end point was objective response (complete or partial response) according to independent central review. Key secondary end points included duration of response, disease control (defined as complete response, partial response, or stable disease), progression-free survival, overall survival, and safety. Exploratory biomarkers were evaluated for their association with response to sotorasib therapy. RESULTS:. CONCLUSIONS:p.G12C-mutated NSCLC. (Funded by Amgen and the National Institutes of Health; CodeBreaK100 ClinicalTrials.gov number, NCT03600883.).